Cover Image

Speed modulated social influence in evacuating pedestrian crowds

Hye Rin Lindsay Lee, Abhishek Bhatia, Jenny Brynjarsdóttir, Nicole Abaid, Alethea Barbaro, Sachit Butail

Abstract


Evacuation is a complex social phenomenon with individuals tending to exit a confined space as soon as possible. Social factors that influence an individual include collision avoidance and conformity with others with respect to the tendency to exit. While collision avoidance has been heavily focused on by the agent-based models used frequently to simulate evacuation scenarios, these models typically assume that all agents have an equal desire to exit the scene in a given situation. It is more likely that, out of those who are exiting, some are patient while others seek to exit as soon as possible. Here, we experimentally investigate the effect of different proportions of patient (no-rush) versus impatient (rush) individuals in an evacuating crowd of up to 24 people. Our results show that a) average speed changes significantly for individuals who otherwise tended to rush (or not rush) with both type of individuals speeding up in the presence of the other; and b) deviation rate, defined as the amount of turning, changes significantly for the rush individuals in the presence of no-rush individuals. We then seek to replicate this effect with Helbing's social force model with the twin purposes of analyzing how well the model fits experimental data, and explaining the differences in speed in terms of model parameters. We find that we must change the interaction parameters for both rush and no-rush agents depending on the condition that we are modeling in order to fit the model to the experimental data.

Keywords


evacuation; experiments; models; mixed intentions

Full Text:

PDF

References


Grosshandler, W.L., Bryner, N.P., Madrzykowski, D., Kuntz, K.: Draft report of the technical investigation of The Station nightclub fire. The Division (2005)

Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., Seyfried, A.: Evacuation dynamics: Empirical results, modeling and applications. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 3142-3176. Springer New York (2009)

Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment 44(3), 437-445 (2009). doi:10.1016/j.buildenv.2008.04.002

Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487-90 (2000)

Karamouzas, I., Skinner, B., Guy, S.J.: Universal power law governing pedestrian interactions. Physical Review Letters 113(23), 238701 (2014)

Henein, C.M., White, T.: Agent-based modelling of forces in crowds. In: Multi-agent and multi-agent-based simulation. Springer (2005)

Yamamoto, K., Kokubo, S., Nishinari, K.: Simulation for pedestrian dynamics by real-coded cellular automata (RCA). Physica A: Statistical Mechanics and its Applications 379(2), 654-660 (2007)

Pelechano, N., Allbeck, J.M., Badler, N.I.: Virtual crowds: Methods, simulation, and control. Synthesis Lectures on Computer Graphics and Animation 3(1), 1-176 (2008)

Helbing, D., Farkas, I.J.: Simulation of pedestrian crowds in normal and evacuation situations. Pedestrian and Evacuation Dynamics 21, 21-58 (2002)

Varas, A., Cornejo, M.D., Mainemer, D., Toledo, B., Rogan, J., Munoz, V., Valdivia, J.A.: Cellular automaton model for evacuation process with obstacles. Physica A: Statistical Mechanics and its Applications 382(2), 631-642 (2007)

Alizadeh, R.: A dynamic cellular automaton model for evacuation process with obstacles. Safety Science 49(2), 315-323 (2011)

Daamen, W., Hoogendoorn, S.: Capacity of doors during evacuation conditions. Procedia Engineering 3, 53-66 (2010)

Nilsson, D., Johansson, A.: Social influence during the initial phase of a fire evacuation--analysis of evacuation experiments in a cinema theatre. Fire Safety Journal 44(1), 71-79 (2009)

Kinateder, M., Warren, W.H.: Social influence on evacuation behavior in real and virtual environments. Frontiers in Robotics and AI 3, 43 (2016)

Heliövaara, S., Ehtamo, H., Helbing, D., Korhonen, T.: Patient and impatient pedestrians in a spatial game for egress congestion. Physical Review E 87(1), 012802 (2013)

Nicolas, A., Bouzat, S., Kuperman, M.N.: Pedestrian flows through a narrow doorway: Effect of individual behaviours on the global flow and microscopic dynamics. Transportation Research Part B: Methodological 99, 30-43 (2017)

Deaton, A.: Height, health, and inequality: the distribution of adult heights in india. American Economic Review 98(2), 468-74 (2008)

Butail, S., Bartolini, T., Porfiri, M.: Collective response of zebrafish shoals to a free-swimming robotic fish. PLoS One 8(10), e76123 (2013). doi:10.1371/journal.pone.0076123

Johansson, F., Duives, D., Daamen, W., Hoogendoorn, S.: The many roles of the relaxation time parameter in force based models of pedestrian dynamics. Transportation Research Procedia 2, 300-308 (2014)

Dewangan, K.N., Owary, C., Datta, R.K.: Anthropometry of male agricultural workers of north-eastern India and its use in design of agricultural tools and equipment. International Journal of Industrial Ergonomics 40(5), 560-573 (2010)

Santner, T.J., Williams, B.J., Notz, W., Williams, B.J.: The design and analysis of computer experiments. Springer (2003)

Villani, C.: Topics in optimal transportation. 58. American Mathematical Soc. (2003)

Carrillo, J.A., Toscani, G.: Wasserstein metric and large-time asymptotics of nonlinear diffusion equations. In: New Trends in Mathematical Physics: In Honour of the Salvatore Rionero 70th Birthday, pp. 234-244. World Scientific (2004)

Peyre, G., Cuturi, M.: Computational optimal transport. Foundations and Trends in Machine Learning 11(5-6), 355-607 (2019)

Pastor, J.M., Garcimartín, A., Gago, P.A., Peralta, J.P., Martín-Gómez, C., Ferrer, L.M., Maza, D., Parisi, D.R., Pugnaloni, L.A., Zuriguel, I.: Experimental proof of faster-is-slower in systems of frictional particles flowing through constrictions. Physical Review E 92(6), 062817 (2015)

Haghani, M., Sarvi, M., Shahhoseini, Z.: When `push'does not come to `shove': Revisiting `faster is slower'in collective egress of human crowds. Transportation Research Part A: Policy and Practice 122, 51-69 (2019)

Siegrist, M., Cvetkovich, G.: Perception of hazards: The role of social trust and knowledge. Risk Analysis 20(5), 713-720 (2000)

Smith, A.: Contribution of perceptions in analysis of walking behavior. Transportation Research Record 2140(1), 128-136 (2009)

Bode, N.W.F., Ronchi, E.: Statistical model fitting and model selection in pedestrian dynamics research. Collective Dynamics 4, 1-32 (2019)




DOI: http://dx.doi.org/10.17815/CD.2020.25

Copyright (c) 2020 Hye Rin Lindsay Lee, Abhishek Bhatia, Jenny Brynjarsdóttir, Nicole Abaid, Alethea Barbaro, Sachit Butail

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.